






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
			import tensorflow as tf

if is_tensorflow_text_available():
			from transformers.models.bert import TFBertTokenizer


_UpperCAmelCase      :		List[str] 					=     ['bert-base-uncased', 'bert-base-cased']
_UpperCAmelCase      :		int 					=     'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():
			class 			lowerCAmelCase_							(					tf.keras.Model       ):




										def __init__( self  : Optional[int]			,				SCREAMING_SNAKE_CASE_  : Optional[Any]						):
														super().__init__()
														lowerCAmelCase__         =    tokenizer
														lowerCAmelCase__         =    AutoConfig.from_pretrained(lowerCAmelCase__						)
														lowerCAmelCase__         =    TFAutoModel.from_config(lowerCAmelCase__						)




										def     __snake_case       ( self  : Tuple			,				SCREAMING_SNAKE_CASE_  : Tuple						):
														lowerCAmelCase__         =    self.tokenizer(lowerCAmelCase__						)
														lowerCAmelCase__         =    self.bert(**lowerCAmelCase__						)
														return out["pooler_output"]
@require_tf
@require_tensorflow_text
class 			lowerCAmelCase_							(					unittest.TestCase       ):




							def     __snake_case       ( self  : str						):
											super().setUp()

											lowerCAmelCase__         =    [
											    BertTokenizer.from_pretrained(lowerCAmelCase__						) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
											]  # repeat for when fast_bert_tokenizer=false
											lowerCAmelCase__         =    [TFBertTokenizer.from_pretrained(lowerCAmelCase__						) for checkpoint in TOKENIZER_CHECKPOINTS] + [
											    TFBertTokenizer.from_pretrained(lowerCAmelCase__			,				use_fast_bert_tokenizer=lowerCAmelCase__						)
											    for checkpoint in TOKENIZER_CHECKPOINTS
											]
											assert len(self.tokenizers						) == len(self.tf_tokenizers						)

											lowerCAmelCase__         =    [
											    '''This is a straightforward English test sentence.''',
											    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
											    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
											    '''And some much more rare Chinese: 齉 堃 齉堃''',
											    '''Je vais aussi écrire en français pour tester les accents''',
											    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
											]
											lowerCAmelCase__         =    list(zip(self.test_sentences			,				self.test_sentences[::-1]						)						)




							def     __snake_case       ( self  : Any						):
											for tokenizer, tf_tokenizer in zip(self.tokenizers			,				self.tf_tokenizers						):
															for test_inputs in (self.test_sentences, self.paired_sentences):
																			lowerCAmelCase__         =    tokenizer(lowerCAmelCase__			,				return_tensors='''tf'''			,				padding='''longest'''						)
																			lowerCAmelCase__         =    tf_tokenizer(lowerCAmelCase__						)

																			for key in python_outputs.keys():
																							self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape						)						)
																							self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]			,				tf.intaa						) == tf_outputs[key]						)						)




							@slow
							def     __snake_case       ( self  : Dict						):
											for tf_tokenizer in self.tf_tokenizers:
															lowerCAmelCase__         =    tf_tokenizer(self.paired_sentences						)
															lowerCAmelCase__         =    tf_tokenizer(
															    text=[sentence[0] for sentence in self.paired_sentences]			,				text_pair=[sentence[1] for sentence in self.paired_sentences]			,				)
															for key in merged_outputs.keys():
																			self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]			,				tf.intaa						) == separated_outputs[key]						)						)




							@slow
							def     __snake_case       ( self  : Union[str, Any]						):
											for tf_tokenizer in self.tf_tokenizers:
															lowerCAmelCase__         =    tf.function(lowerCAmelCase__						)
															for test_inputs in (self.test_sentences, self.paired_sentences):
																			lowerCAmelCase__         =    tf.constant(lowerCAmelCase__						)
																			lowerCAmelCase__         =    compiled_tokenizer(lowerCAmelCase__						)
																			lowerCAmelCase__         =    tf_tokenizer(lowerCAmelCase__						)

																			for key in eager_outputs.keys():
																							self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]						)						)




							@slow
							def     __snake_case       ( self  : int						):
											for tf_tokenizer in self.tf_tokenizers:
															lowerCAmelCase__         =    ModelToSave(tokenizer=lowerCAmelCase__						)
															lowerCAmelCase__         =    tf.convert_to_tensor(self.test_sentences						)
															lowerCAmelCase__         =    model(lowerCAmelCase__						)  # Build model with some sample inputs
															with TemporaryDirectory() as tempdir:
																			lowerCAmelCase__         =    Path(lowerCAmelCase__						) / '''saved.model'''
																			model.save(lowerCAmelCase__						)
																			lowerCAmelCase__         =    tf.keras.models.load_model(lowerCAmelCase__						)
															lowerCAmelCase__         =    loaded_model(lowerCAmelCase__						)
															# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
															self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output						)						)			,				1e-5						)










'''simple docstring'''



from __future__ import annotations
def        UpperCAmelCase (    A	:					list[int]       ,		A	:					int					):
							if len(A					) < k or k < 0:
														raise ValueError('''Invalid Input'''					)
							SCREAMING_SNAKE_CASE       :   Dict         =      sum(array[:k]					)
							for i in range(len(A					) - k					):
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      current_sum - array[i] + array[i + k]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      max(A       ,		A					)
							return max_sum


if __name__ == "__main__":
							from doctest import testmod
							from random import randint

							testmod()
							lowerCAmelCase_  :							Optional[int]             =    [randint(-1000, 1000) for i in range(100)]
							lowerCAmelCase_  :							Optional[int]             =    randint(0, 110)
							print(f'The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}')




